NeuroMI: Progressive EEG Foundation Modeling for Neural Decoding of Motor Intention

Motor imagery (MI) provides a non-invasive paradigm for decoding motor intention-related neural states through brain-computer interfaces (BCIs). However, the practical deployment of MI-based BCIs remains constrained by data scarcity, substantial inter-subject variability, and poor cross-dataset generalization. Existing methods either train task-specific models from scratch on small MI datasets, which compromises robustness, or directly fine-tune generic electroencephalography (EEG) pre-trained models, resulting in a significant domain gap between generic representations and MI-specific neural signatures. This paper proposes a hierarchical three-stage foundation model framework, NeuroMI, to systematically address this gap. In the first stage, a general-purpose EEG encoder is designed and pre-trained on large-scale EEG corpora via self-supervised learning, yielding transferable spatio-temporal-spectral representations. In the second stage, we build a multi-dataset MI-specific EEG corpus (approximately 300,000 samples) and specialize the encoder on it with neurophysiologically motivated objectives, explicitly steering the model toward motor-relevant neural dynamics. In the third stage, the specialized encoder is adapted to downstream MI tasks using lightweight task-specific heads, enabling efficient transfer across subjects and datasets. Experiments on multiple public MI datasets demonstrate that the proposed framework consistently outperforms both generic EEG foundation models and conventional MI-specific architectures. Beyond achieving improved decoding accuracy, the model yields interpretable representations that align with established MI neurophysiology. These results provide a practical pathway toward robust MI decoding in real-world BCI applications.